Income Level and Impaired Kidney Function Among Working Adults in Japan

Key Points Question Is there an association between income levels and the development of impaired kidney function among the working population in a country with an established universal health care system? Findings This retrospective cohort study of 5.6 million adults found that those in the lowest compared with the highest income decile showed increased risk of rapid chronic kidney disease (CKD) progression and kidney replacement therapy initiation. A negative monotonic association with rapid CKD progression was more evident among males and individuals without diabetes. Meaning These findings indicate that income-based disparities are associated with the development of impaired kidney function in the context of universal health care, highlighting the crucial role of comprehensive CKD prevention and management strategies for low-income workers.

eTable 6. Slope and relative index of inequalities for impaired kidney function by income levels eTable 7. Absolute risk difference for rapid CKD progression between the 1 st to 9 th decile and the 10 th decile eTable 8. Population attributable risks of rapid chronic kidney disease (CKD) progression and kidney replacement therapy (KRT) initiation by setting income levels at top 10 th and 50 th percentiles This supplemental material has been provided by the authors to give readers additional information about their work.eMethod 1. Database of the Japan Health Insurance Association The Japan Health Insurance Association (JHIA) is the largest public medical insurer in Japan, covering approximately 40% (30 million) of the working-age population.To qualify for the JHIA coverage, individuals need to work a certain number of hours at these companies.They lose this entitlement if they leave their job, for example, to start a new job at a large company or due to health issues such as illness.Members of the JHIA are eligible to receive an annual health check-up, as mandated to be provided by insurers by law.The participation rate for health checkups among JHIA-insured individuals aged 40 to 74 years (target ages for specific health checkups to prevent lifestyle diseases mandated by law), after excluding those who joined or withdrew during the year and those who had difficulty taking the check-ups due to long-term hospitalization or other reasons, was 53.8% (2015). 1 References: 1. Ministry of Health, Labour and Welfare.Implementation Status of Specific Health Checkups and Specific Health Guidance in Fiscal Year 2015.Updated July 31, 2017.https://www.mhlw.go.jp/file/04-Houdouhappyou-12401000-Hokenkyoku-Soumuka/0000173093.pdf(Japanese).Accessed May 24, 2023.

eMethod 2. Details in eGFR calculation
The eGFR was calculated using a creatinine formula modified for Japanese individuals: eGFR = 194×serum creatinine -1.094 ×age -0.287 × α (α = 0.739 for women, and α = 1 for men). 1 We adopted a single eGFR measurement from the first health check-up of each individual in each fiscal year.
The annual decline in eGFR (mL/min/1.73m 2 ) was calculated based on the interval between the baseline check-up in the fiscal year 2015 and the check-up in the latest fiscal year during the fiscal years 2016 to 2021 for each individual.We distinguished continuous dialysis from temporary dialysis according to whether individuals underwent dialysis for at least two consecutive months.We right-censored individuals when they died or withdrew from insurance, due to job termination or other reasons.

eMethod 3. Sensitivity analyses
We conducted the following three sensitivity analyses.First, to account for residual confounding we adjusted for urinary protein levels (categorical 5-level variables based on random spot urinalysis; -, ±, 1+, 2+, 3+) and baseline eGFR values in the regression models.Second, to address the selection bias by excluding individuals due to missing covariate data and laboratory outliers, we conducted an analysis using the Inverse Probability Weighting (IPW) approach. 1ird, to account for changes in income levels over the study period, we reanalyzed the data using the average income for the study period instead of the individual incomes in the fiscal year 2015 as an exposure.

eMethod 4. Additional analyses
We also conducted three additional analyses.First, to assess the heterogeneity by the urinary protein level, we conducted the subgroup analyses by baseline urinary protein levels: (-), (±) vs.
(1+) or above.Second, to assess the degree of inequalities more formally, we calculated the Slope Index of Inequality (SII), 1 Relative Index of Inequality (RII), 2 and Kunst Mackenbach Relative Index (KMI). 3For rapid CKD progression and KRT initiation, we calculated these indices using the prevalence (per 10,000 persons) and incidence (per million person-years), respectively.Third, to simulate the population impacts of the increased CKD disparities across income levels, we calculated adjusted absolute risk difference for rapid CKD progression and population attributable risk (PAR) for rapid CKD progression and KRT initiation, assuming the highest income group (top 10 th percentile) or those above the median income (top 50 th percentile) as an unexposed population.The volumes were adjusted for age, sex, smoking, BMI, waist circumference, hemoglobin, systolic blood pressure, LDL-cholesterol, HDL-cholesterol, triglyceride, blood glucose, uric acid, hypertension, cardiovascular disease, cancer, dyslipidemia, hyperuricemia, and prefecture.We found an association between income levels and both development of CKD and progression of existing CKD.The trend of association was different for each outcome by the baseline CKD stage.P-for-interaction was detected as .06for rapid CKD progression and 0.45 for initiation of KRT.

References
eGFR= estimated glomerular filtration rate, CKD=chronic kidney disease, KRT=kidney replacement therapy, BMI=body mass index, LDL=low-density lipoprotein and HDL=high-density lipoprotein eFigure 4. Sensitivity analysis additionally adjusting for urinary protein levels (A) Adjusted odds ratio for rapid CKD progression by income levels (B) Adjusted hazard ratio for initiation of kidney replacement therapy (KRT) by income levels.Y-axis shows the log scale of the odds ratio (A) and the hazard ratio (B).
The sample size was reduced to 5,566,791 due to the missing data of urinary protein, rapid CKD progression was observed in 322,222 cases (5.8 %) and KRT initiation was observed in 4,958 cases (0.1 %).eGFR= estimated glomerular filtration rate, CKD=chronic kidney disease and KRT=kidney replacement therapy eFigure 5. Sensitivity analysis additionally adjusting for baseline eGFR (A) Adjusted odds ratio for rapid CKD progression by income levels (B) Adjusted hazard ratio for initiation of kidney replacement therapy (KRT) by income levels.Y-axis shows the log scale of the odds ratio (A) and the hazard ratio (B).eGFR= estimated glomerular filtration rate, CKD=chronic kidney disease and KRT=kidney replacement therapy eFigure 6. Sensitivity analysis using inverse probability weighting approach for missing data of covariates and outliers in laboratory data The volumes were adjusted for age, sex, smoking, BMI, waist circumference, hemoglobin, systolic blood pressure, LDL-cholesterol, HDL-cholesterol, triglyceride, blood glucose, uric acid, hypertension, cardiovascular disease, cancer, dyslipidemia, hyperuricemia, and prefecture.The association was clear negative monotonic risk increasing in those without proteinuria while the trends were less clear in those with proteinuria.P-for-interaction was detected as .01 for rapid CKD progression and .31for initiation of KRT.
eGFR= estimated glomerular filtration rate, CKD=chronic kidney disease, KRT=kidney replacement therapy eTable 1. Demographic characteristics of the study population by individual income levels

eFigure 1 .
Flow of study sample selection Cre=Creatinine, eGFR=estimated glomerular filtration rate eFigure 2. The estimated average estimated glomerular filtration rate (eGFR) decline volume per year by individual income levels The model was adjusted for age, sex, smoking, BMI, waist circumference, hemoglobin, systolic blood pressure, LDL-cholesterol, HDL-cholesterol, triglyceride, blood glucose, uric acid, diabetes, hypertension, cardiovascular disease, cancer, dyslipidemia, hyperuricemia, and prefecture.The lowest income groups showed the largest decline in eGFR.eGFR=estimated glomerular filtration rate, BMI=body mass index, LDL=low-density lipoprotein and HDL=high-density lipoprotein eFigure 3. Subgroup analysis for the association between income and impaired kidney function by baseline CKD stage (A,B) Adjusted odds ratio for rapid CKD progression by income levels (C,D) Adjusted hazard ratio for the initiation of kidney replacement therapy (KRT) by income levels.Y-axis shows the log scale of the odds ratio (A, B) and the hazard ratio (C, D).
(A) Adjusted odds ratio for rapid CKD progression by income levels (B) Adjusted hazard ratio for the initiation of kidney replacement therapy (KRT) by income levels.Y-axis shows the log scale of the odds ratio (A) and the hazard ratio (B).eGFR= estimated glomerular filtration rate, CKD=chronic kidney disease, KRT=kidney replacement therapy eFigure 7. Sensitivity analysis according to average annual income during the study period (A) Adjusted odds ratio for rapid CKD progression by income levels (B) Adjusted hazard ratio for the initiation of kidney replacement therapy (KRT) by income levels.Y-axis shows the log scale of the odds ratio (A) and the hazard ratio (B).eGFR= estimated glomerular filtration rate, CKD=chronic kidney disease, KRT=kidney replacement therapy eFigure 8. Subgroup analysis for the association between income and impaired kidney function by urinary protein levels (A, B) Adjusted odds ratio for rapid CKD progression by income levels.(C,D) Adjusted hazard ratio for the initiation of kidney replacement therapy (KRT) by income levels.Y-axis shows the log scale of the odds ratio (A, B) and the hazard ratio (C, D).
USD=the United States dollar, SD=standard deviation, BMI=body mass index, eGFR=estimated glomerular filtration rate, Hb=hemoglobin, BP=blood pressure, LDL-C=low-density lipoprotein cholesterol, HDL-C=high-density lipoprotein cholesterol, TG=triglyceride and UA=uric acid eTable 2. Demographic characteristics of the study population by individual income levels among men Demographic characteristics of the study population by individual income levels among women USD=the United States dollar, SD=standard deviation, BMI=body mass index, eGFR=estimated glomerular filtration rate, Hb=hemoglobin, BP=blood pressure, LDL-C=low-density lipoprotein cholesterol, HDL-C=high-density lipoprotein cholesterol, TG=triglyceride and UA=uric acid eTable 4. Demographic characteristics of the study population undertaking health check-ups and not undertaking health check-ups a. age in April,2015 b.P-value was calculated by independent sample's t-test and Pearson's chi-square test Results of the proportional hazard tests based on Schoenfeld residuals between income deciles SII=slope index of inequality, RII=relative index of inequality, KMI=Kunst Mackenbach relative index, eGFR=estimated glomerular filtration rate, CKD=chronic kidney disease, KRT=kidney replacement therapy and CI=confidence intervaleTable 7. Absolute risk difference for rapid CKD progression between the 1 st to 9 th decile and the 10 th decile Population attributable risks of rapid CKD progression and KRT initiation by setting income levels at top 10 th and 50 th percentiles